Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Artificial intelligence and mobile robots
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Interactive POMDPs: Properties and Preliminary Results
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Information and Self-Organization: A Macroscopic Approach to Complex Systems (Springer Series in Synergetics)
Self organized UAV swarm planning optimization for search and destroy using SWARMFARE simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
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Many sectors of the military are interested in Self-Organized (SO) systems because of their flexibility, versatility and economics. The military is researching and employing autonomous and swarming ground robots, Unmanned Aerial Vehicles (UAVs) and Water Vehicles, medical agents, and 'Cyber-craft' security agents. The processes for effectively developing these systems are still in their infancy. Currently, little effort is focused on building simple agent rules with low-level SO systems communication in order to facilitate emergent behaviors. Note that only with the use of effective control structures can the full potential of these systems realized. Presented is an innovative new paradigm for developing SO-based autonomous vehicles. Using a formal design model, the Interactive Partially Observable Markov Decision Process, a full understanding of this SO domain is possible. With this design model and a focused effort on the minimization of computational and informational complexity, emergent entangled control hierarchies allow the SO rules to operate efficiently and effectively. This work extends the formal model decomposition technique, and in doing so ties in the information theoretic optimization to develop emergent structures. Preliminary computational results reflect limited success.